Blood Test Could Predict Who Is Likely to Develop Psychotic Disorders

Summary: A new blood test could help doctors monitor those who are at risk of developing psychiatric disorders. The test looks for specific protein biomarkers in blood samples of those with risk factors for psychiatric illnesses and can help to predict who is most likely to develop psychosis in the future.

Source: RCSI

Scientists have discovered that testing the levels of certain proteins in blood samples can predict whether a person at risk of psychosis is likely to develop a psychotic disorder years later.

The study is published in the current edition of JAMA Psychiatry and was led by researchers from RCSI University of Medicine and Health Sciences.

Based on certain criteria, such as mild or brief psychotic symptoms, some people are considered to be clinically at high risk of developing a psychotic disorder, such as schizophrenia. However, only 20% to 30% of these people will actually go on to develop a psychotic disorder.

The researchers analysed blood samples taken from people at clinical high risk of psychosis. These individuals were followed up for several years to see who did and did not develop a psychotic disorder.

After assessing the proteins in blood samples and using machine learning to analyse this data, the scientists were able to find patterns of proteins in the early blood samples that could predict who did and did not develop a psychotic disorder at follow-up.

Many of these proteins are involved in inflammation, suggesting that there are early changes in the immune system in people who go on to develop a psychotic disorder. The findings also suggest that it is possible to predict their outcomes using blood samples taken several years in advance.

The most accurate test was based on the 10 most predictive proteins. It correctly identified those who would go on to develop a psychotic disorder in 93% of high-risk cases, and it correctly identified those who would not in 80% of cases.

This shows blood vials
Many of these proteins are involved in inflammation, suggesting that there are early changes in the immune system in people who go on to develop a psychotic disorder. Image is in the public domain.

“Ideally, we would like to prevent psychotic disorders, but that requires being able to accurately identify who is most at risk,” said Professor David Cotter, the study’s senior and corresponding author and professor of molecular psychiatry at RCSI.

“Our research has shown that, with help from machine learning, analysis of protein levels in blood samples can predict who is at truly at risk and could possibly benefit from preventive treatments. We now need to study these markers in other people at high risk of psychosis to confirm these findings.”

A patent application has been filed, and the research team is working to commercialise this research through licensing or partnering with industry.

Funding: This research was funded by the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) Project (Project EU-GEI) from the European Community’s Seventh Framework Programme, by the UK Medical Research Council and by the Irish Health Research Board.

David Mongan, RCSI PhD student and Irish Clinical Academic Training (ICAT) Fellow, analysed the data with the supervision of Professor David Cotter and Professor Mary Cannon from the RCSI Department of Psychiatry. The ICAT programme is supported by the Wellcome Trust and the Health Research Board, the Health Service Executive National Doctors Training and Planning and the Health and Social Care, Research and Development Division, Northern Ireland. The blood samples were analysed in the UCD Conway Institute under the supervision of Dr Gerard Cagney.

About this psychology research article

Source:
RCSI
Contacts:
Michael Sullivan – RCSI
Image Source:
The image is in the public domain.

Original Research: Open access
“Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence” by David Cotter et al. JAMA Psychiatry.


Abstract

Development of Proteomic Prediction Models for Transition to Psychotic Disorder in the Clinical High-Risk State and Psychotic Experiences in Adolescence

Importance
Biomarkers that are predictive of outcomes in individuals at risk of psychosis would facilitate individualized prognosis and stratification strategies.

Objective
To investigate whether proteomic biomarkers may aid prediction of transition to psychotic disorder in the clinical high-risk (CHR) state and adolescent psychotic experiences (PEs) in the general population.

Design, Setting, and Participants
This diagnostic study comprised 2 case-control studies nested within the European Network of National Schizophrenia Networks Studying Gene-Environment Interactions (EU-GEI) and the Avon Longitudinal Study of Parents and Children (ALSPAC). EU-GEI is an international multisite prospective study of participants at CHR referred from local mental health services. ALSPAC is a United Kingdom–based general population birth cohort. Included were EU-GEI participants who met CHR criteria at baseline and ALSPAC participants who did not report PEs at age 12 years. Data were analyzed from September 2018 to April 2020.

Main Outcomes and Measures
In EU-GEI, transition status was assessed by the Comprehensive Assessment of At-Risk Mental States or contact with clinical services. In ALSPAC, PEs at age 18 years were assessed using the Psychosis-Like Symptoms Interview. Proteomic data were obtained from mass spectrometry of baseline plasma samples in EU-GEI and plasma samples at age 12 years in ALSPAC. Support vector machine learning algorithms were used to develop predictive models.

Results
The EU-GEI subsample (133 participants at CHR (mean [SD] age, 22.6 [4.5] years; 68 [51.1%] male) comprised 49 (36.8%) who developed psychosis and 84 (63.2%) who did not. A model based on baseline clinical and proteomic data demonstrated excellent performance for prediction of transition outcome (area under the receiver operating characteristic curve [AUC], 0.95; positive predictive value [PPV], 75.0%; and negative predictive value [NPV], 98.6%). Functional analysis of differentially expressed proteins implicated the complement and coagulation cascade. A model based on the 10 most predictive proteins accurately predicted transition status in training (AUC, 0.99; PPV, 76.9%; and NPV, 100%) and test (AUC, 0.92; PPV, 81.8%; and NPV, 96.8%) data. The ALSPAC subsample (121 participants from the general population with plasma samples available at age 12 years (61 [50.4%] male) comprised 55 participants (45.5%) with PEs at age 18 years and 61 (50.4%) without PEs at age 18 years. A model using proteomic data at age 12 years predicted PEs at age 18 years, with an AUC of 0.74 (PPV, 67.8%; and NPV, 75.8%).

Conclusions and Relevance
In individuals at risk of psychosis, proteomic biomarkers may contribute to individualized prognosis and stratification strategies. These findings implicate early dysregulation of the complement and coagulation cascade in the development of psychosis outcomes.

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